Professional Experience

  • Present 2020

    Senior Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2021 2020

    Research Fellow

    LIRNEasia,
    Sri Lanka

  • 2020 2014

    Graduate Research/Teaching Fellow

    University of Oregon, Department of Computer and Information Science,
    USA.

  • 2018 2018

    Givens Associate

    Argonne National Laboratory,
    USA.

  • 2020 2011

    Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2014 2013

    Researcher

    LIRNEasia,
    Sri Lanka

  • 2014 2013

    Visiting Lecturer

    Northshore College of Business and Technology,
    Sri Lanka

Education

  • Ph.D. 2020

    Ph.D. in Computer & Information Science

    University of Oregon, USA

  • MS 2016

    MS in Computer & Information Science

    University of Oregon, USA

  • BSc2011

    B.Sc Engineering (Hons)in Computer Science & Engineering

    University of Moratuwa, Sri Lanka

Featured Research

SHADE: Semantic Hypernym Annotator for Domain-Specific Entities-Dungeons and Dragons Domain Use Case


A. Peiris, and N. de Silva

2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS), IEEE, 2023, pp. 1--6,

Manual data annotation is an important NLP task but one that takes a considerable amount of resources and effort. In spite of the costs, labelling and categorizing entities are essential for NLP tasks such as semantic evaluation. Even though annotation can be done by non-experts in most cases, due to the fact that this requires human labour, the process is costly. Another major challenge encountered in data annotation is maintaining annotation consistency. Annotation efforts are typically carried out by teams of multiple annotators. The annotations need to maintain consistency in relation to both the domain truth and annotation format while reducing human errors. Annotating a specialized domain that deviates significantly from the general domain, such as fantasy literature, will see a significant amount of human error and annotator disagreement. So it is vital that proper guidelines and error reduction mechanisms are enforced. One way to enforce these constraints is by using a specialized application. Such an app can ensure that the notations are consistent, and the labels can be pre-defined or restricted reducing the room for errors. In this paper, we present SHADE, an annotation software that can be used to annotate entities in the high fantasy literature domain Dungeons and Dragons extracted from the Forgotten Realms Fandom Wiki.